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Influencing dynamics on social networks without knowledge of network microstructure
Matthew Garrod1, Nick S Jones2
1Department of Mathematics, Imperial College London, London SW7 2AZ, UK.
Journal of the Royal Society, Interface
|August 25, 2021
Summary
Social network campaigns can promote health behaviors and reduce polarization. New methods leverage individual attributes for influence, bypassing the need for complete network data, making campaigns more efficient and privacy-preserving.
Area of Science:
- Computational Social Science
- Statistical Mechanics
- Network Science
Background:
- Social network interventions are effective for behavior change and polarization mitigation.
- Complete social network data is often unavailable due to privacy and logistical constraints.
- Individual attributes can infer network position and opinions.
Purpose of the Study:
- To investigate strategies for influencing opinion formation in social networks using statistical mechanics.
- To develop scalable methods for coarse-grained influence strategies on large, modular networks.
- To explore the Ising influence problem in the presence of ambient social fields.
Main Methods:
- Utilized statistical mechanics-based models, specifically Ising models with modular structures.
- Employed synthetic and data-based examples to test influence strategies.
- Investigated the impact of external (ambient) fields on networked opinion dynamics.
Main Results:
- Demonstrated the advantages of coarse-grained influence strategies on modular Ising models.
- Showcased a scalable methodology for influencing large networked systems.
- Highlighted that strong ambient fields simplify the control of networked dynamics.
Conclusions:
- Public information campaigns can be designed using social network insights without invasive data collection.
- Coarse-grained influence strategies offer an efficient alternative to full network knowledge.
- This approach enables scalable and privacy-preserving public opinion management.
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